1.1 Field of the Invention
The invention relates to an efficient system for autonomous determination of the left ventricle (LV) epicardial and endocardial borders of a human heart. Echocardiographic image sequences are acquired from an apical four-chamber view of the heart or from an apical two-chamber view and subsequently processed to provide near-real-time approximations of endocardial borders and estimates of wall thickness.
1.2 Description of Related Art
1.2.1 Ultrasound Imaging
Two-dimensional ultrasonic imaging is used as an important non-invasive technique in the comprehensive characterization of a number of body organs. In ultrasonic imaging, a sound pulse is emitted from a transducer towards the organ being imaged. The pulse attenuates and then reflects when it hits a medium with an acoustic impedance different from that of the medium in which the pulse is traveling. The time the sound pulse takes in transit is a measure of the distance of the boundary from the transducer, and the amount of energy that is reflected is a measure of the difference of the acoustic impedance across the boundary. (In practice, because the energy of the pulse diminishes as it travels, post-processing of the reflected signal includes time gain control that compensates the attenuation of the signal over time). Assuming the pulse travels at a single speed in the body, and by emitting pulses across a plane, a two-dimensional record of the received energy in spatial coordinates represents a cross-sectional view of the imaged organ.
One advantage of ultrasound over other imaging modalities is its ability to generate real-time images of anatomy without using ionizing radiation. Historically, due to the relatively poor image quality and number of artifacts, ultrasound has not been used extensively for assessing physiology or organ function. The estimation of organ dimensions is a standard procedure, and velocity information from Doppler can be used to infer volume flow. However these measurements are time consuming and suffer from a large degree of operator variability. Despite these problems, efforts continue at practically applying ultrasonic techniques to a variety of applications.
1.2.2 Echocardiography
Echocardiography is the application of ultrasonic imaging to the heart. Echocardiography has experienced widespread acceptance in the evaluation of cardiac disease and in characterizing the structure and function of the heart. This acceptance is in large part due to its non-invasive nature, and its real-time capability for observing both cardiac structure and motion. A considerable amount of quantitative echocardiographic information can be obtained concerning cardiac anatomy, chamber diameter and volume, wall thickness, valve, and ejection fraction. The interest to the practicing physician is that if the endocardial border has been accurately approximated, then chamber cross-sectional area can be estimated. If the endocardial border has been accurately approximated at both end diastole (ED) and end systole (ES) then not only can the fractional area change (FAC) be calculated as the difference divided by the area at end-diastole, but the motion (or excursion) of the walls can be objectively characterized, as well. If, in addition, the epicardial border has been accurately found, then wall thickness estimates can be made to more objectively assess muscle in a possible infarct zone.
The real-time capability of echocardiography can be used to measure variation in the shape of heart structures throughout the cardiac cycle. These analyses require the complete determination of inner (endocardial) and outer (epicardial) boundaries of the heart wall and particularly those of the LV. Present evidence indicates that sensitive detection of ischemic disease with two-dimensional echocardiography requires knowledge of the endocardial border on echocardiographic frames throughout the cardiac cycle as well as at end-diastole and end-systole (Weyman et al., 1984).
Because both global and regional left ventricular function are major variables used to determine prognosis in cardiac disease, there is considerable interest in the ability to quantitate function indexes from echocardiographic images. Presently, such indices (e.g., left ventricular chamber volume and left ventricular ejection fraction) are calculated from observer-defined cardiac boundaries traced on either the imaging device or an offline analysis. Tracing endocardial borders on two-dimensional echocardiograms is tedious and the selected borders are highly subjective. Indeed, in most systematic studies, substantial intra-observer and interobserver variability has been found in such observer-defined cardiac boundaries.
Current methodology relies on human observers to draw boundaries of the epicardial and endocardial borders in order to calculate indices of heart function and health. Typically, in current ultrasound machines, a track-ball is incorporated as the drawing implement. Using this track ball a cursor can be moved beginning at the mitral valve (MV) annulus (MVA) around the endocardial contour to the opposite MVA position and accepted. Once this is accepted, the volume can be calculated from a single plane prolate ellipsoid model or a model of summation of discs. This procedure is performed at both ED and ES to calculate ejection fraction (EF), i.e., EF=((VEDxe2x88x92VES)/VED)xc3x97100%. If the view that is approximately perpendicular to the current apical view is also available, the same drawing procedure can be used on the respective end diastolic or end systolic frame and a biplane summation of disc method used to supply the volumes for the above calculation of EF. In off-line analysis systems, the same type of drawing tool is implemented although usually the drawing implement is a mouse instead of the track ball.
In the last few years, the field of echocardiography has undergone rapid change, with advances in image quality and the advent of new imaging methods (Entrekin, R et al., 1999; Lees, W., 1999; Leen, E., 1999; Becher et al., 1999). These new applications can be split into two categories:
1) Automatic assistance for measurements that are typically done manually to both improve repeatability, and reduce exam time. Examples include the tracing of the borders of the LV to enable calculations of parameters such as ejection fraction and cardiac output and the tracing of the intima-media complex to assist with the early detection of atherosclerosis.
2) Determination of new diagnostic information from many of the newly developed modes. Examples here include estimation of organ perfusion and the use of Doppler processing in tissue to assess cardiac wall motion or myocardial compliance.
In both cases, the development of new quantitative techniques requires extended clinical testing to ensure not only the technical relevance of the measurements, but also the clinical efficacy, particularly for new applications.
There are several currently available automated analysis echocardiographic systems. One is an ultrasound machine marketed by Hewlett-Packard (Sonos, 5500 Imaging System), While this system incorporates many automatic features, it is very gain- and operator-dependent. With this dependence on operator control or gain settings, many of the issues concerning inter and intra-observer variability remain unresolved. A similar system is offered by GE/Vingmed, which has met with similar lack of success.
1.3 Deficiencies in the Prior Art
Current procedures are time consuming, often requiring several minutes to obtain at least a rough estimate of an endocardial border. In many cases, time is an urgent consideration in assessing the status of heart patients and decisions may be made without information that would lead to a more informed decision on the most appropriate procedure. An additional disadvantage is the large inter-observer and intra-observer variability in measuring the parameters used to determine heart chamber boundaries. A great deal of xe2x80x9cdropoutxe2x80x9d can occur in certain areas of the endocardial contour. Depending on the observer, these dropout regions may be visually interpolated with varying experience and care. When the images are viewed in real time motion, small targets can be seen to move in specific patterns that suggest to the observer that they are associated with the endocardium and are thus part of this boundary. When the images are frozen and the motion information is lost to the observer, it becomes very difficult to pick which of the numerous small points of brightness on the screen had been the ones associated with the appropriate motion pattern. Thus, especially in marginal images, the resulting inter- and intra-observer variability can be quite large. This is demonstrated by the reported increase in variability associated with the perceived image quality as reported (Geiser, 1988).
Manually defining such boundaries becomes increasingly labor intensive when the analysis of a complete cardiac cycle is needed to provide a description of the systolic and diastolic wall motion pattern, or when a number of echocardiographic frames have to be processed in order to obtain a long period time-history of cardiac function. Unfortunately, automating the identification of boundary regions in echocardiograms using computers is often difficult because of the poor quality of the echocardiographic images. The lack of clear definition of the boundary regions is due to the intrinsic limitations of echo imaging, such as low image intensity contrast, signal dropout in the image, and boundary discontinuity in any given frame. xe2x80x9cDropoutxe2x80x9d occurs where sound waves are reflected from two different levels in a structure and the reflected waves arrive simultaneously, but out of phase, at the face of the transducer, causing a cancellation of their amplitudes. Thus, no return signal is perceived at that depth.
The poor quality of echocardiograms is also attributable to reverberations of the initial sound pulse, and xe2x80x9cspecklexe2x80x9d noise, caused by the back scattering of the incident wave front after it hits tissue microstructures. This phenomenon superimposes a very fine texture, a xe2x80x9csalt and pepperxe2x80x9d pattern, on the image. Another limitation of echocardiographic imaging is that sound reflection is not very pronounced when the angle between a boundary of the heart and the propagation path of the sound pulse is small. Hence, the lateral wall (LW) boundaries of the heart are usually not very well defined in echocardiographic images. Thus, in imaging the LV; typically the anterior and posterior cardiac walls are most well defined.
In the past several years, advances in computer data processing technology have allowed the application of several different automatic boundary detection methods to echocardiographic images. However, most researchers have had difficulties with image enhancement and boundary detection with echocardiographic images because of the low signal-to-noise ratio and large discontinuities in such images. Thus, automated border detection has been reported in two-dimensional echocardiographic images, but only when the images are of good quality and certain smoothing techniques are employed prior to edge detection in order to render the endocardial edge more continuous. An overview of the field has been described in Kerber (1988).
It is therefore desirable to automate as much as possible the determination of boundaries of echocardiographic images. Automated definition of the boundaries would improve the reliability of the quantitative analysis by eliminating the subjectivity of manual tracing. Consequently, there is a need for a method to automatically determine quantitative characteristics of ultrasonic images, especially echocardiographic images. In particular, there is also a need for a method that will automatically determine the center of an imaged structure and approximate the borders of such a structure. With respect to echocardiographic images, there is a need for an automated system that can determine the center of the LV, approximate both the endocardial and epicardial borders, and estimate cardiac wall motion without user input. In addition, it is also desirable to automatically detect the presence of a flattened interventricular septum caused by pressure or volume overload from the RV.
The present invention is directed to resolving one or all of these problems mentioned above.
The invention comprises a method and apparatus for generating a synthetic echocardiographic image. The method comprises first obtaining, for a plurality of pathologically similar reference hearts, a reference echocardiographic image of each reference heart at ES and at ED. Next, the coupled epicardial and endocardial borders are identified in each echocardiographic image. An epicardial/endocardial border pair is then modeled from the identified borders. The method then locates a plurality of predetermined features in the reference echocardiographic images. The predetermined features are then located in the subject echocardiographic image from the location of the predetermined features in the reference echocardiographic images. The modeled epicardial/endocardial border pair is then mapped onto the subject echocardiographic image relative to the location of the predetermined features in the subject echocardiographic image.
The apparatus generally comprises an echocardiographic machine for obtaining the echocardiographic images that are then processed by a computing system. In one aspect of the invention, the invention comprises such a computing system programmed to perform the autonomous portions of the method. In another aspect, the invention comprises a program storage medium encoded with instructions that perform the autonomous portions of the method when executed by a computer.